Radio channel measurement is a widely investigated research area. The research has aimed at the development of channel models such as Typical Urban, Rural Area etc. Each such model describes the characteristics various channel environments have and which may be very different from each other indeed. Such models of real measurements can i.a. be utilized as an input parameter in the planning of a radio cell structure.
The differences in the radio environments come from the physical properties of the channel. As propagating radio waves are reflected, diffracted and scattered depending on the dimension and surface properties of the obstacles they encounter, various environments will affect a transmitted signal differently. The effects of reflection and scattering will lead to a multi-path propagation of each sent signal, i.e. each sent signal will be split in numerous rays which all travel on their own path to the receiver. Since these paths will have unequal distance, the received signal will be dispersed in time. This is commonly referred to time dispersion. Different environments will introduce various amount of time dispersion. Hence, a sparsely built rural area will lead to less time dispersion than a densely built urban environment.
A signal sent from a transceiver at some time will start to arrive at the receiver at, say time t0. From that point on, the received energy will be the sum of all incoming rays as a function of the excess time τ. The amount of time dispersion that the channel induces affects the time it takes before the received energy fades away. The power delay profile of a channel displays the received energy as a function of excess time. Using the power delay profile computation of mean excess delay, and root mean square (rms) delay spread can be performed. Mean excess delay is a measurement of the extra delay that the channel introduces after the first part of the signal arrives at t0. Delay spread is the standard deviation of the delayed reflections, weighted by their respective energy. Both mean excess delay and delay spread differs widely between channel types which makes them important channel characteristics.
The instantaneous received power is the sum of many rays arriving with different amplitude and phase. Hence, a moving antenna will experience a strong signal where the superposition of the rays is constructive and, unfortunately, a very weak signal if it's destructive. These variations in time are usually referred to as fading.
The Rayleigh model assumes that a received multi-path signal can be considered consisting of a large number of waves, possibly infinitely many, with independent and identically distributed, (i.i.d.) in-phase and quadrature components. The central limit theorem supports, that with sufficiently many arriving waves the IQ components will be Gaussian distributed.
If z=x+iy, where x and y are i.i.d Gaussian distributed with zero mean and variance σ^2, the probability density function, PDF, for the received amplitude, |z|, becomes
      f    ⁡          (              x        ❘        σ            )        =            x              σ        2              ⁢          exp              (                  -                                    x              2                                      2              ⁢                                                          ⁢                              σ                2                                                    )            which is the Rayleigh distribution. It is well known that the Rayleigh model is in fact suitable for describing how the amplitude of the received signal fades in areas with lots of scattered waves, such as densely built cities.
Radio waves propagating in sparsely built cities or rural areas are, just like those in densely areas, scattered and reflected. The big difference is that, in contrast to radio waves in a densely built city, usually a line-of-sight (LOS) wave reaches the receiver. Since this wave often is strong compared to the scattered waves, the PDF of the amplitude will change. The scattered waves will no longer have zero mean.
Due to this shift in mean, the amplitude PDF will change form. This new form is the Rician distribution defined as
      f    ⁡          (                        x          ❘          s                ,        σ            )        =                    x                  σ          2                    ⁢              exp                  (                      -                                          (                                                      x                    2                                    +                                      s                    2                                                  )                                            2                ⁢                                                                  ⁢                                  σ                  2                                                              )                    ⁢                        I          0                ⁡                  (                      xs                          σ              2                                )                    ⁢                          ⁢      x        >    0  where the non-centrality parameter s>0 and the scale parameter σ>0. I0 is the zero-order modified Bessel function of the first kind. The Rician K-factor which is defined as
  K  =            s      2              2      ⁢                          ⁢              σ        2            express the ratio of direct wave component to the scattered waves. The stronger the line-of-sight component is, the greater will the shift of mean be for the scattered waves. Such a shift will make the Rician distribution approach Gaussian distribution. As the direct wave part weakens the shift of mean will approach zero and the Rician PDF becomes equal to the Rayleigh PDF.
Existing measurement devices for determining radio channel characteristics are very complex and expensive. This equipment typically requires certain types of radio signals and is typically developed with the aim to generate radio channel models. For example existing measurement devices usually employ a specific transmitter and receiver.
Furthermore, when planning a radio system for an area it is important to have knowledge about the radio channel characteristics for the different parts of the area in order to optimize the radio system performance. Hence, for each cell it is important to know which radio channel model, such as Typical Urban, Rural Area, etc to employ as input in the cell planning tool.
A visual observation can give a hint of the environment type, i.e. if the measurements are performed in a city or in a rural area. However, even if two areas look to be very similar, the radio wave propagation properties can differ significantly. This will therefore result in that radio cells are designed using an incorrect radio channel model which in turn results in a degraded performance compared to if the true radio channel characteristics had been employed.
In order to avoid problems resulting from application of an incorrect radio channel model in cell planning it is hence desired to have a true picture of the radio channel characteristics at hand. Also, without information relating to the radio environment it can be hard to explain differences between obtained results or to select the correct radio channel model for a particular area. Furthermore the radio channel information should preferably be provided rapidly and be inexpensive to generate.